Trends on Computer Security: Cryptography, User Authentication, Denial of Service and Intrusion Detection

The new generation of security threats has beenpromoted by digital currencies and real-time applications, whereall users develop new ways to communicate on the Internet.Security has evolved in the need of privacy and anonymity forall users and his portable devices. New technologies in everyfield prove that users need security features integrated into theircommunication applications, parallel systems for mobile devices,internet, and identity management. This review presents the keyconcepts of the main areas in computer security and how it hasevolved in the last years. This work focuses on cryptography,user authentication, denial of service attacks, intrusion detectionand firewalls

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

Reviewing the effectiveness of artificial intelligence techniques against cyber security risks

The rapid increase in malicious cyber-criminal activities has made the field of cybersecurity a crucial research discipline. Over the areas, the advancement in information technology has enabled cybercriminals to launch increasingly sophisticated attacks that can endanger cybersecurity. Due to this, traditional cybersecurity solutions have become ineffective against emerging cyberattacks. However, the advent of Artificial Intelligence (AI) – particularly Machine Learning (ML) and Deep Learning (DL) – and cryptographic techniques have shown promising results in countering the evolving cyber threats caused by adversaries. Therefore, in this study, AI's potential in enhancing cybersecurity solutions is discussed. Additionally, the study has provided an in-depth analysis of different AI-based techniques that can detect, analyse, and prevent cyber threats. In the end, the present study has also discussed future research opportunities that are linked with the development of AI systems in the field of cybersecurity

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI\u27s potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains

Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage

Anomaly detection is an important activity in cloud computing systems because it aids in the identification of odd behaviours or actions that may result in software glitch, security breaches, and performance difficulties. Detecting aberrant resource utilization trends in virtual machines is a typical application of anomaly detection in cloud computing (VMs). Currently, the most serious cyber threat is distributed denial-of-service attacks. The afflicted server\u27s resources and internet traffic resources, such as bandwidth and buffer size, are slowed down by restricting the server\u27s capacity to give resources to legitimate customers. To recognize attacks and common occurrences, machine learning techniques such as Quadratic Support Vector Machines (QSVM), Random Forest, and neural network models such as MLP and Autoencoders are employed. Various machine learning algorithms are used on the optimised NSL-KDD dataset to provide an efficient and accurate predictor of network intrusions. In this research, we propose a neural network based model and experiment on various central and spiral rearrangements of the features for distinguishing between different types of attacks and support our approach of better preservation of feature structure with image representations. The results are analysed and compared to existing models and prior research. The outcomes of this study have practical implications for improving the security and performance of cloud computing systems, specifically in the area of identifying and mitigating network intrusions

Bio-inspired network security for 5G-enabled IoT applications

Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks